Abstract
Background: Where self-report is unfeasible or observations are difficult, physiological estimates of pain are needed. Methods: Pain-data from 30 healthy adults were gathered to create a database of physiological pain responses. A model was then developed, to analyze pain-data and visualize the AI-estimated level of pain on a mobile app. Results: The initial low precision and F1-score of the pain classification algorithm were resolved by interpolating a percentage of similar data. Discussion: This system presents a novel approach to assess pain in noncommunicative people with the use of a sensor sock, AI predictor and mobile app. Performance analysis and the limitations of the AI algorithm are discussed.
| Original language | English |
|---|---|
| Article number | 2250047 |
| Pages (from-to) | 1-11 |
| Number of pages | 11 |
| Journal | International journal of neural systems |
| Volume | 32 |
| Issue number | 10 |
| Early online date | 9 Sept 2022 |
| DOIs | |
| Publication status | Published - Oct 2022 |
Bibliographical note
Funding Information:The authors would like to acknowledge the work of Ir. Bart van Dijk and Ir. Paul van Beek on programming the SID Pain App.
Publisher Copyright:
© World Scientific Publishing Company.
Funding
The authors would like to acknowledge the work of Ir. Bart van Dijk and Ir. Paul van Beek on programming the SID Pain App.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Mobile application
- pain measurement
- random forest prediction
- smart sock wearable
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